Overview

Dataset statistics

Number of variables21
Number of observations3376
Missing cells13129
Missing cells (%)18.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory554.0 KiB
Average record size in memory168.0 B

Variable types

Categorical6
Numeric13
Text2

Alerts

NumberofBuildings is highly overall correlated with ThirdLargestPropertyUseTypeHigh correlation
PropertyGFATotal is highly overall correlated with PropertyGFABuilding(s) and 3 other fieldsHigh correlation
PropertyGFABuilding(s) is highly overall correlated with PropertyGFATotal and 3 other fieldsHigh correlation
LargestPropertyUseTypeGFA is highly overall correlated with PropertyGFATotal and 3 other fieldsHigh correlation
SecondLargestPropertyUseTypeGFA is highly overall correlated with PropertyGFATotal and 3 other fieldsHigh correlation
ThirdLargestPropertyUseTypeGFA is highly overall correlated with SecondLargestPropertyUseTypeGFAHigh correlation
TotalGHGEmissions is highly overall correlated with PropertyGFATotal and 3 other fieldsHigh correlation
GHGEmissionsIntensity is highly overall correlated with TotalGHGEmissionsHigh correlation
BuildingType is highly overall correlated with PrimaryPropertyTypeHigh correlation
PrimaryPropertyType is highly overall correlated with BuildingTypeHigh correlation
ThirdLargestPropertyUseType is highly overall correlated with NumberofBuildingsHigh correlation
SecondLargestPropertyUseType is highly imbalanced (55.5%)Imbalance
ComplianceStatus is highly imbalanced (83.1%)Imbalance
SecondLargestPropertyUseType has 1697 (50.3%) missing valuesMissing
SecondLargestPropertyUseTypeGFA has 1697 (50.3%) missing valuesMissing
ThirdLargestPropertyUseType has 2780 (82.3%) missing valuesMissing
ThirdLargestPropertyUseTypeGFA has 2780 (82.3%) missing valuesMissing
YearsENERGYSTARCertified has 3257 (96.5%) missing valuesMissing
ENERGYSTARScore has 843 (25.0%) missing valuesMissing
NumberofBuildings is highly skewed (γ1 = 43.39499472)Skewed
PropertyGFATotal is highly skewed (γ1 = 24.12940742)Skewed
PropertyGFABuilding(s) is highly skewed (γ1 = 27.62439064)Skewed
LargestPropertyUseTypeGFA is highly skewed (γ1 = 30.09595071)Skewed
NumberofBuildings has 92 (2.7%) zerosZeros
PropertyGFAParking has 2872 (85.1%) zerosZeros
SecondLargestPropertyUseTypeGFA has 126 (3.7%) zerosZeros
ThirdLargestPropertyUseTypeGFA has 48 (1.4%) zerosZeros

Reproduction

Analysis started2023-06-12 08:18:43.813797
Analysis finished2023-06-12 08:20:34.920086
Duration1 minute and 51.11 seconds
Software versionydata-profiling vv4.2.0
Download configurationconfig.json

Variables

BuildingType
Categorical

Distinct8
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size26.5 KiB
nonresidential
1460 
multifamily lr (1-4)
1018 
multifamily mr (5-9)
580 
multifamily hr (10+)
 
110
sps-district k-12
 
98
Other values (3)
 
110

Length

Max length20
Median length20
Mean length17.167358
Min length6

Characters and Unicode

Total characters57957
Distinct characters30
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rownonresidential
2nd rownonresidential
3rd rownonresidential
4th rownonresidential
5th rownonresidential

Common Values

ValueCountFrequency (%)
nonresidential 1460
43.2%
multifamily lr (1-4) 1018
30.2%
multifamily mr (5-9) 580
 
17.2%
multifamily hr (10+) 110
 
3.3%
sps-district k-12 98
 
2.9%
nonresidential cos 85
 
2.5%
campus 24
 
0.7%
nonresidential wa 1
 
< 0.1%

Length

2023-06-12T10:20:35.367914image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-12T10:20:36.172713image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
multifamily 1708
24.5%
nonresidential 1546
22.2%
lr 1018
14.6%
1-4 1018
14.6%
mr 580
 
8.3%
5-9 580
 
8.3%
hr 110
 
1.6%
10 110
 
1.6%
sps-district 98
 
1.4%
k-12 98
 
1.4%
Other values (3) 110
 
1.6%

Most occurring characters

ValueCountFrequency (%)
i 6704
 
11.6%
l 5980
 
10.3%
n 4638
 
8.0%
m 4020
 
6.9%
3600
 
6.2%
t 3450
 
6.0%
r 3352
 
5.8%
a 3279
 
5.7%
e 3092
 
5.3%
s 1949
 
3.4%
Other values (20) 17893
30.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 45425
78.4%
Decimal Number 3612
 
6.2%
Space Separator 3600
 
6.2%
Dash Punctuation 1794
 
3.1%
Close Punctuation 1708
 
2.9%
Open Punctuation 1708
 
2.9%
Math Symbol 110
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 6704
14.8%
l 5980
13.2%
n 4638
10.2%
m 4020
8.8%
t 3450
7.6%
r 3352
7.4%
a 3279
7.2%
e 3092
6.8%
s 1949
 
4.3%
u 1732
 
3.8%
Other values (9) 7229
15.9%
Decimal Number
ValueCountFrequency (%)
1 1226
33.9%
4 1018
28.2%
5 580
16.1%
9 580
16.1%
0 110
 
3.0%
2 98
 
2.7%
Space Separator
ValueCountFrequency (%)
3600
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1794
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1708
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1708
100.0%
Math Symbol
ValueCountFrequency (%)
+ 110
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 45425
78.4%
Common 12532
 
21.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 6704
14.8%
l 5980
13.2%
n 4638
10.2%
m 4020
8.8%
t 3450
7.6%
r 3352
7.4%
a 3279
7.2%
e 3092
6.8%
s 1949
 
4.3%
u 1732
 
3.8%
Other values (9) 7229
15.9%
Common
ValueCountFrequency (%)
3600
28.7%
- 1794
14.3%
) 1708
13.6%
( 1708
13.6%
1 1226
 
9.8%
4 1018
 
8.1%
5 580
 
4.6%
9 580
 
4.6%
0 110
 
0.9%
+ 110
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 57957
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 6704
 
11.6%
l 5980
 
10.3%
n 4638
 
8.0%
m 4020
 
6.9%
3600
 
6.2%
t 3450
 
6.0%
r 3352
 
5.8%
a 3279
 
5.7%
e 3092
 
5.3%
s 1949
 
3.4%
Other values (20) 17893
30.9%
Distinct24
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size26.5 KiB
low-rise multifamily
987 
mid-rise multifamily
564 
small- and mid-sized office
293 
other
256 
warehouse
187 
Other values (19)
1089 

Length

Max length27
Median length22
Mean length17.189277
Min length5

Characters and Unicode

Total characters58031
Distinct characters29
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowhotel
2nd rowhotel
3rd rowhotel
4th rowhotel
5th rowhotel

Common Values

ValueCountFrequency (%)
low-rise multifamily 987
29.2%
mid-rise multifamily 564
16.7%
small- and mid-sized office 293
 
8.7%
other 256
 
7.6%
warehouse 187
 
5.5%
large office 173
 
5.1%
k-12 school 139
 
4.1%
mixed use property 133
 
3.9%
high-rise multifamily 105
 
3.1%
retail store 91
 
2.7%
Other values (14) 448
13.3%

Length

2023-06-12T10:20:37.083349image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
multifamily 1656
23.6%
low-rise 987
14.1%
mid-rise 564
 
8.0%
office 508
 
7.2%
small 293
 
4.2%
and 293
 
4.2%
mid-sized 293
 
4.2%
other 256
 
3.6%
warehouse 199
 
2.8%
large 173
 
2.5%
Other values (28) 1794
25.6%

Most occurring characters

ValueCountFrequency (%)
i 7607
13.1%
l 5597
 
9.6%
m 4764
 
8.2%
e 4535
 
7.8%
3640
 
6.3%
r 3355
 
5.8%
s 3179
 
5.5%
a 3045
 
5.2%
o 2881
 
5.0%
f 2811
 
4.8%
Other values (19) 16617
28.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 51664
89.0%
Space Separator 3640
 
6.3%
Dash Punctuation 2409
 
4.2%
Decimal Number 278
 
0.5%
Other Punctuation 40
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 7607
14.7%
l 5597
10.8%
m 4764
9.2%
e 4535
8.8%
r 3355
 
6.5%
s 3179
 
6.2%
a 3045
 
5.9%
o 2881
 
5.6%
f 2811
 
5.4%
t 2796
 
5.4%
Other values (14) 11094
21.5%
Decimal Number
ValueCountFrequency (%)
1 139
50.0%
2 139
50.0%
Space Separator
ValueCountFrequency (%)
3640
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2409
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 40
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 51664
89.0%
Common 6367
 
11.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 7607
14.7%
l 5597
10.8%
m 4764
9.2%
e 4535
8.8%
r 3355
 
6.5%
s 3179
 
6.2%
a 3045
 
5.9%
o 2881
 
5.6%
f 2811
 
5.4%
t 2796
 
5.4%
Other values (14) 11094
21.5%
Common
ValueCountFrequency (%)
3640
57.2%
- 2409
37.8%
1 139
 
2.2%
2 139
 
2.2%
/ 40
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 58031
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 7607
13.1%
l 5597
 
9.6%
m 4764
 
8.2%
e 4535
 
7.8%
3640
 
6.3%
r 3355
 
5.8%
s 3179
 
5.5%
a 3045
 
5.2%
o 2881
 
5.0%
f 2811
 
4.8%
Other values (19) 16617
28.6%

Neighborhood
Categorical

Distinct14
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size26.5 KiB
downtown
573 
east
453 
magnolia / queen anne
423 
greater duwamish
375 
northeast
280 
Other values (9)
1272 

Length

Max length22
Median length16
Mean length10.11404
Min length4

Characters and Unicode

Total characters34145
Distinct characters21
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowdowntown
2nd rowdowntown
3rd rowdowntown
4th rowdowntown
5th rowdowntown

Common Values

ValueCountFrequency (%)
downtown 573
17.0%
east 453
13.4%
magnolia / queen anne 423
12.5%
greater duwamish 375
11.1%
northeast 280
8.3%
lake union 251
7.4%
northwest 221
 
6.5%
north 187
 
5.5%
southwest 166
 
4.9%
central 134
 
4.0%
Other values (4) 313
9.3%

Length

2023-06-12T10:20:37.811905image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
downtown 573
10.9%
east 453
 
8.6%
magnolia 423
 
8.0%
423
 
8.0%
queen 423
 
8.0%
anne 423
 
8.0%
greater 375
 
7.1%
duwamish 375
 
7.1%
northeast 280
 
5.3%
union 251
 
4.8%
Other values (9) 1273
24.1%

Most occurring characters

ValueCountFrequency (%)
n 4163
12.2%
e 3790
11.1%
a 3498
10.2%
t 3246
9.5%
o 2772
 
8.1%
w 1908
 
5.6%
1896
 
5.6%
s 1852
 
5.4%
r 1791
 
5.2%
h 1326
 
3.9%
Other values (11) 7903
23.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 31826
93.2%
Space Separator 1896
 
5.6%
Other Punctuation 423
 
1.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 4163
13.1%
e 3790
11.9%
a 3498
11.0%
t 3246
10.2%
o 2772
8.7%
w 1908
 
6.0%
s 1852
 
5.8%
r 1791
 
5.6%
h 1326
 
4.2%
u 1310
 
4.1%
Other values (9) 6170
19.4%
Space Separator
ValueCountFrequency (%)
1896
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 423
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 31826
93.2%
Common 2319
 
6.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 4163
13.1%
e 3790
11.9%
a 3498
11.0%
t 3246
10.2%
o 2772
8.7%
w 1908
 
6.0%
s 1852
 
5.8%
r 1791
 
5.6%
h 1326
 
4.2%
u 1310
 
4.1%
Other values (9) 6170
19.4%
Common
ValueCountFrequency (%)
1896
81.8%
/ 423
 
18.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 34145
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 4163
12.2%
e 3790
11.1%
a 3498
10.2%
t 3246
9.5%
o 2772
 
8.1%
w 1908
 
5.6%
1896
 
5.6%
s 1852
 
5.4%
r 1791
 
5.2%
h 1326
 
3.9%
Other values (11) 7903
23.1%

YearBuilt
Real number (ℝ)

Distinct113
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1968.5732
Minimum1900
Maximum2015
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.5 KiB
2023-06-12T10:20:38.550574image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1900
5-th percentile1908
Q11948
median1975
Q31997
95-th percentile2012
Maximum2015
Range115
Interquartile range (IQR)49

Descriptive statistics

Standard deviation33.088156
Coefficient of variation (CV)0.016808192
Kurtosis-0.87134177
Mean1968.5732
Median Absolute Deviation (MAD)24
Skewness-0.53944456
Sum6645903
Variance1094.8261
MonotonicityNot monotonic
2023-06-12T10:20:39.298604image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2000 72
 
2.1%
2014 67
 
2.0%
1989 67
 
2.0%
2008 66
 
2.0%
1988 64
 
1.9%
1999 64
 
1.9%
1968 63
 
1.9%
1990 60
 
1.8%
2001 60
 
1.8%
2002 59
 
1.7%
Other values (103) 2734
81.0%
ValueCountFrequency (%)
1900 55
1.6%
1901 8
 
0.2%
1902 11
 
0.3%
1903 4
 
0.1%
1904 15
 
0.4%
1905 9
 
0.3%
1906 19
 
0.6%
1907 31
0.9%
1908 27
0.8%
1909 32
0.9%
ValueCountFrequency (%)
2015 37
1.1%
2014 67
2.0%
2013 51
1.5%
2012 35
1.0%
2011 15
 
0.4%
2010 24
 
0.7%
2009 41
1.2%
2008 66
2.0%
2007 42
1.2%
2006 45
1.3%

NumberofBuildings
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct17
Distinct (%)0.5%
Missing8
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean1.1068884
Minimum0
Maximum111
Zeros92
Zeros (%)2.7%
Negative0
Negative (%)0.0%
Memory size26.5 KiB
2023-06-12T10:20:39.968878image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q31
95-th percentile1
Maximum111
Range111
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.1084018
Coefficient of variation (CV)1.9048007
Kurtosis2205.2962
Mean1.1068884
Median Absolute Deviation (MAD)0
Skewness43.394995
Sum3728
Variance4.4453579
MonotonicityNot monotonic
2023-06-12T10:20:40.553660image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
1 3175
94.0%
0 92
 
2.7%
2 37
 
1.1%
3 22
 
0.7%
4 12
 
0.4%
5 10
 
0.3%
6 5
 
0.1%
8 3
 
0.1%
10 2
 
0.1%
9 2
 
0.1%
Other values (7) 8
 
0.2%
(Missing) 8
 
0.2%
ValueCountFrequency (%)
0 92
 
2.7%
1 3175
94.0%
2 37
 
1.1%
3 22
 
0.7%
4 12
 
0.4%
5 10
 
0.3%
6 5
 
0.1%
7 1
 
< 0.1%
8 3
 
0.1%
9 2
 
0.1%
ValueCountFrequency (%)
111 1
 
< 0.1%
27 1
 
< 0.1%
23 1
 
< 0.1%
16 1
 
< 0.1%
14 2
0.1%
11 1
 
< 0.1%
10 2
0.1%
9 2
0.1%
8 3
0.1%
7 1
 
< 0.1%

NumberofFloors
Real number (ℝ)

Distinct50
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.7091232
Minimum0
Maximum99
Zeros16
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size26.5 KiB
2023-06-12T10:20:41.256277image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median4
Q35
95-th percentile12
Maximum99
Range99
Interquartile range (IQR)3

Descriptive statistics

Standard deviation5.4944648
Coefficient of variation (CV)1.1667702
Kurtosis55.950645
Mean4.7091232
Median Absolute Deviation (MAD)2
Skewness5.9223397
Sum15898
Variance30.189143
MonotonicityNot monotonic
2023-06-12T10:20:42.022120image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4 692
20.5%
3 692
20.5%
1 466
13.8%
2 439
13.0%
6 306
9.1%
5 295
8.7%
7 148
 
4.4%
8 64
 
1.9%
10 32
 
0.9%
11 32
 
0.9%
Other values (40) 210
 
6.2%
ValueCountFrequency (%)
0 16
 
0.5%
1 466
13.8%
2 439
13.0%
3 692
20.5%
4 692
20.5%
5 295
8.7%
6 306
9.1%
7 148
 
4.4%
8 64
 
1.9%
9 18
 
0.5%
ValueCountFrequency (%)
99 1
 
< 0.1%
76 1
 
< 0.1%
63 1
 
< 0.1%
56 1
 
< 0.1%
55 1
 
< 0.1%
49 1
 
< 0.1%
47 1
 
< 0.1%
46 1
 
< 0.1%
42 6
0.2%
41 3
0.1%

PropertyGFATotal
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct3195
Distinct (%)94.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean94833.537
Minimum11285
Maximum9320156
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.5 KiB
2023-06-12T10:20:42.817528image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum11285
5-th percentile21291.5
Q128487
median44175
Q390992
95-th percentile320096
Maximum9320156
Range9308871
Interquartile range (IQR)62505

Descriptive statistics

Standard deviation218837.61
Coefficient of variation (CV)2.3075972
Kurtosis946.23949
Mean94833.537
Median Absolute Deviation (MAD)19739.5
Skewness24.129407
Sum3.2015802 × 108
Variance4.7889898 × 1010
MonotonicityNot monotonic
2023-06-12T10:20:43.649073image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36000 9
 
0.3%
25920 8
 
0.2%
28800 7
 
0.2%
21600 7
 
0.2%
24000 6
 
0.2%
22320 4
 
0.1%
30720 4
 
0.1%
30240 4
 
0.1%
43380 3
 
0.1%
31900 3
 
0.1%
Other values (3185) 3321
98.4%
ValueCountFrequency (%)
11285 1
< 0.1%
11685 1
< 0.1%
11968 1
< 0.1%
12294 1
< 0.1%
12769 1
< 0.1%
13157 1
< 0.1%
13661 1
< 0.1%
14101 1
< 0.1%
15398 1
< 0.1%
16000 1
< 0.1%
ValueCountFrequency (%)
9320156 1
< 0.1%
2200000 1
< 0.1%
1952220 1
< 0.1%
1765970 1
< 0.1%
1605578 1
< 0.1%
1592914 1
< 0.1%
1585960 1
< 0.1%
1536606 1
< 0.1%
1400000 2
0.1%
1380959 1
< 0.1%

PropertyGFAParking
Real number (ℝ)

Distinct496
Distinct (%)14.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8001.5261
Minimum0
Maximum512608
Zeros2872
Zeros (%)85.1%
Negative0
Negative (%)0.0%
Memory size26.5 KiB
2023-06-12T10:20:44.465824image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile46400.75
Maximum512608
Range512608
Interquartile range (IQR)0

Descriptive statistics

Standard deviation32326.724
Coefficient of variation (CV)4.0400698
Kurtosis58.974892
Mean8001.5261
Median Absolute Deviation (MAD)0
Skewness6.6511908
Sum27013152
Variance1.0450171 × 109
MonotonicityNot monotonic
2023-06-12T10:20:45.239709image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2872
85.1%
13320 3
 
0.1%
10800 2
 
0.1%
20416 2
 
0.1%
30000 2
 
0.1%
22000 2
 
0.1%
100176 2
 
0.1%
25800 2
 
0.1%
12960 2
 
0.1%
756 1
 
< 0.1%
Other values (486) 486
 
14.4%
ValueCountFrequency (%)
0 2872
85.1%
38 1
 
< 0.1%
260 1
 
< 0.1%
415 1
 
< 0.1%
604 1
 
< 0.1%
756 1
 
< 0.1%
800 1
 
< 0.1%
919 1
 
< 0.1%
1263 1
 
< 0.1%
1392 1
 
< 0.1%
ValueCountFrequency (%)
512608 1
< 0.1%
407795 1
< 0.1%
389860 1
< 0.1%
368980 1
< 0.1%
335109 1
< 0.1%
327680 1
< 0.1%
319400 1
< 0.1%
303707 1
< 0.1%
285688 1
< 0.1%
285000 1
< 0.1%

PropertyGFABuilding(s)
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct3193
Distinct (%)94.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean86832.011
Minimum3636
Maximum9320156
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.5 KiB
2023-06-12T10:20:46.031830image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum3636
5-th percentile21021
Q127756
median43216
Q384276.25
95-th percentile282658.5
Maximum9320156
Range9316520
Interquartile range (IQR)56520.25

Descriptive statistics

Standard deviation207939.81
Coefficient of variation (CV)2.3947368
Kurtosis1161.3603
Mean86832.011
Median Absolute Deviation (MAD)18958.5
Skewness27.624391
Sum2.9314487 × 108
Variance4.3238965 × 1010
MonotonicityNot monotonic
2023-06-12T10:20:46.757035image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36000 9
 
0.3%
25920 8
 
0.2%
21600 7
 
0.2%
28800 7
 
0.2%
24000 6
 
0.2%
30240 4
 
0.1%
22320 4
 
0.1%
30720 4
 
0.1%
25800 3
 
0.1%
31900 3
 
0.1%
Other values (3183) 3321
98.4%
ValueCountFrequency (%)
3636 1
< 0.1%
10925 1
< 0.1%
11285 1
< 0.1%
11440 1
< 0.1%
11685 1
< 0.1%
11968 1
< 0.1%
12294 1
< 0.1%
12769 1
< 0.1%
12806 1
< 0.1%
13157 1
< 0.1%
ValueCountFrequency (%)
9320156 1
< 0.1%
2200000 1
< 0.1%
1765970 1
< 0.1%
1632820 1
< 0.1%
1592914 1
< 0.1%
1400000 1
< 0.1%
1380959 1
< 0.1%
1323055 1
< 0.1%
1258280 1
< 0.1%
1215718 1
< 0.1%
Distinct466
Distinct (%)13.8%
Missing9
Missing (%)0.3%
Memory size26.5 KiB
2023-06-12T10:20:47.967736image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length255
Median length162
Mean length25.934363
Min length5

Characters and Unicode

Total characters87321
Distinct characters31
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique314 ?
Unique (%)9.3%

Sample

1st rowhotel
2nd rowhotel, parking, restaurant
3rd rowhotel
4th rowhotel
5th rowhotel, parking, swimming pool
ValueCountFrequency (%)
multifamily 1707
17.2%
housing 1707
17.2%
parking 1087
11.0%
office 958
 
9.7%
store 472
 
4.8%
other 419
 
4.2%
retail 404
 
4.1%
warehouse 278
 
2.8%
non-refrigerated 261
 
2.6%
181
 
1.8%
Other values (97) 2430
24.5%
2023-06-12T10:20:49.988359image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
i 9379
 
10.7%
6537
 
7.5%
e 5738
 
6.6%
o 5413
 
6.2%
a 5307
 
6.1%
r 5210
 
6.0%
l 5017
 
5.7%
t 4958
 
5.7%
n 4445
 
5.1%
u 4299
 
4.9%
Other values (21) 31018
35.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 76720
87.9%
Space Separator 6537
 
7.5%
Other Punctuation 3059
 
3.5%
Dash Punctuation 633
 
0.7%
Decimal Number 294
 
0.3%
Open Punctuation 39
 
< 0.1%
Close Punctuation 39
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 9379
12.2%
e 5738
 
7.5%
o 5413
 
7.1%
a 5307
 
6.9%
r 5210
 
6.8%
l 5017
 
6.5%
t 4958
 
6.5%
n 4445
 
5.8%
u 4299
 
5.6%
f 4191
 
5.5%
Other values (12) 22763
29.7%
Other Punctuation
ValueCountFrequency (%)
, 2683
87.7%
/ 364
 
11.9%
& 12
 
0.4%
Decimal Number
ValueCountFrequency (%)
1 147
50.0%
2 147
50.0%
Space Separator
ValueCountFrequency (%)
6537
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 633
100.0%
Open Punctuation
ValueCountFrequency (%)
( 39
100.0%
Close Punctuation
ValueCountFrequency (%)
) 39
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 76720
87.9%
Common 10601
 
12.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 9379
12.2%
e 5738
 
7.5%
o 5413
 
7.1%
a 5307
 
6.9%
r 5210
 
6.8%
l 5017
 
6.5%
t 4958
 
6.5%
n 4445
 
5.8%
u 4299
 
5.6%
f 4191
 
5.5%
Other values (12) 22763
29.7%
Common
ValueCountFrequency (%)
6537
61.7%
, 2683
25.3%
- 633
 
6.0%
/ 364
 
3.4%
1 147
 
1.4%
2 147
 
1.4%
( 39
 
0.4%
) 39
 
0.4%
& 12
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 87321
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 9379
 
10.7%
6537
 
7.5%
e 5738
 
6.6%
o 5413
 
6.2%
a 5307
 
6.1%
r 5210
 
6.0%
l 5017
 
5.7%
t 4958
 
5.7%
n 4445
 
5.1%
u 4299
 
4.9%
Other values (21) 31018
35.5%
Distinct56
Distinct (%)1.7%
Missing20
Missing (%)0.6%
Memory size26.5 KiB
2023-06-12T10:20:51.104390image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length52
Median length19
Mean length16.255959
Min length5

Characters and Unicode

Total characters54555
Distinct characters31
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)0.3%

Sample

1st rowhotel
2nd rowhotel
3rd rowhotel
4th rowhotel
5th rowhotel
ValueCountFrequency (%)
multifamily 1667
27.1%
housing 1667
27.1%
office 543
 
8.8%
warehouse 211
 
3.4%
non-refrigerated 199
 
3.2%
other 179
 
2.9%
store 140
 
2.3%
k-12 139
 
2.3%
school 139
 
2.3%
facility 100
 
1.6%
Other values (79) 1168
19.0%
2023-06-12T10:20:53.067707image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
i 6836
 
12.5%
l 4161
 
7.6%
u 3825
 
7.0%
o 3805
 
7.0%
m 3642
 
6.7%
f 3116
 
5.7%
t 3096
 
5.7%
e 3058
 
5.6%
a 2831
 
5.2%
2796
 
5.1%
Other values (21) 17389
31.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 50806
93.1%
Space Separator 2796
 
5.1%
Dash Punctuation 445
 
0.8%
Decimal Number 278
 
0.5%
Other Punctuation 196
 
0.4%
Open Punctuation 17
 
< 0.1%
Close Punctuation 17
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 6836
13.5%
l 4161
 
8.2%
u 3825
 
7.5%
o 3805
 
7.5%
m 3642
 
7.2%
f 3116
 
6.1%
t 3096
 
6.1%
e 3058
 
6.0%
a 2831
 
5.6%
s 2666
 
5.2%
Other values (12) 13770
27.1%
Other Punctuation
ValueCountFrequency (%)
/ 166
84.7%
, 20
 
10.2%
& 10
 
5.1%
Decimal Number
ValueCountFrequency (%)
1 139
50.0%
2 139
50.0%
Space Separator
ValueCountFrequency (%)
2796
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 445
100.0%
Open Punctuation
ValueCountFrequency (%)
( 17
100.0%
Close Punctuation
ValueCountFrequency (%)
) 17
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 50806
93.1%
Common 3749
 
6.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 6836
13.5%
l 4161
 
8.2%
u 3825
 
7.5%
o 3805
 
7.5%
m 3642
 
7.2%
f 3116
 
6.1%
t 3096
 
6.1%
e 3058
 
6.0%
a 2831
 
5.6%
s 2666
 
5.2%
Other values (12) 13770
27.1%
Common
ValueCountFrequency (%)
2796
74.6%
- 445
 
11.9%
/ 166
 
4.4%
1 139
 
3.7%
2 139
 
3.7%
, 20
 
0.5%
( 17
 
0.5%
) 17
 
0.5%
& 10
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 54555
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 6836
 
12.5%
l 4161
 
7.6%
u 3825
 
7.0%
o 3805
 
7.0%
m 3642
 
6.7%
f 3116
 
5.7%
t 3096
 
5.7%
e 3058
 
5.6%
a 2831
 
5.2%
2796
 
5.1%
Other values (21) 17389
31.9%

LargestPropertyUseTypeGFA
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct3122
Distinct (%)93.0%
Missing20
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean79177.639
Minimum5656
Maximum9320156
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.5 KiB
2023-06-12T10:20:53.802956image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum5656
5-th percentile17609
Q125094.75
median39894
Q376200.25
95-th percentile243388.5
Maximum9320156
Range9314500
Interquartile range (IQR)51105.5

Descriptive statistics

Standard deviation201703.41
Coefficient of variation (CV)2.5474795
Kurtosis1320.6098
Mean79177.639
Median Absolute Deviation (MAD)17574
Skewness30.095951
Sum2.6572016 × 108
Variance4.0684265 × 1010
MonotonicityNot monotonic
2023-06-12T10:20:54.557075image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22000 9
 
0.3%
24000 9
 
0.3%
30000 8
 
0.2%
21600 8
 
0.2%
20000 7
 
0.2%
25000 6
 
0.2%
28800 5
 
0.1%
45000 5
 
0.1%
36000 5
 
0.1%
15000 5
 
0.1%
Other values (3112) 3289
97.4%
(Missing) 20
 
0.6%
ValueCountFrequency (%)
5656 1
< 0.1%
6455 1
< 0.1%
6601 1
< 0.1%
6900 1
< 0.1%
7245 1
< 0.1%
7387 1
< 0.1%
7501 1
< 0.1%
7583 1
< 0.1%
7758 1
< 0.1%
8061 1
< 0.1%
ValueCountFrequency (%)
9320156 1
< 0.1%
1719643 1
< 0.1%
1680937 1
< 0.1%
1639334 1
< 0.1%
1585960 1
< 0.1%
1350182 1
< 0.1%
1314475 1
< 0.1%
1191115 1
< 0.1%
1172127 1
< 0.1%
1072000 1
< 0.1%

SecondLargestPropertyUseType
Categorical

IMBALANCE  MISSING 

Distinct50
Distinct (%)3.0%
Missing1697
Missing (%)50.3%
Memory size26.5 KiB
parking
976 
office
215 
retail store
155 
other
 
59
restaurant
 
40
Other values (45)
234 

Length

Max length52
Median length7
Mean length9.1774866
Min length5

Characters and Unicode

Total characters15409
Distinct characters31
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11 ?
Unique (%)0.7%

Sample

1st rowparking
2nd rowparking
3rd rowparking
4th rowparking
5th rowparking

Common Values

ValueCountFrequency (%)
parking 976
28.9%
office 215
 
6.4%
retail store 155
 
4.6%
other 59
 
1.7%
restaurant 40
 
1.2%
non-refrigerated warehouse 33
 
1.0%
multifamily housing 18
 
0.5%
fitness center/health club/gym 17
 
0.5%
supermarket/grocery store 14
 
0.4%
data center 13
 
0.4%
Other values (40) 139
 
4.1%
(Missing) 1697
50.3%

Length

2023-06-12T10:20:55.353383image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
parking 976
45.5%
office 228
 
10.6%
store 170
 
7.9%
retail 155
 
7.2%
other 94
 
4.4%
restaurant 40
 
1.9%
37
 
1.7%
warehouse 35
 
1.6%
non-refrigerated 33
 
1.5%
services 20
 
0.9%
Other values (74) 355
 
16.6%

Most occurring characters

ValueCountFrequency (%)
r 1846
12.0%
i 1649
10.7%
a 1507
9.8%
n 1258
 
8.2%
e 1205
 
7.8%
g 1093
 
7.1%
p 1030
 
6.7%
k 1007
 
6.5%
t 776
 
5.0%
o 724
 
4.7%
Other values (21) 3314
21.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 14751
95.7%
Space Separator 464
 
3.0%
Other Punctuation 89
 
0.6%
Dash Punctuation 79
 
0.5%
Decimal Number 12
 
0.1%
Open Punctuation 7
 
< 0.1%
Close Punctuation 7
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 1846
12.5%
i 1649
11.2%
a 1507
10.2%
n 1258
8.5%
e 1205
8.2%
g 1093
7.4%
p 1030
 
7.0%
k 1007
 
6.8%
t 776
 
5.3%
o 724
 
4.9%
Other values (12) 2656
18.0%
Other Punctuation
ValueCountFrequency (%)
/ 73
82.0%
, 14
 
15.7%
& 2
 
2.2%
Decimal Number
ValueCountFrequency (%)
1 6
50.0%
2 6
50.0%
Space Separator
ValueCountFrequency (%)
464
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 79
100.0%
Open Punctuation
ValueCountFrequency (%)
( 7
100.0%
Close Punctuation
ValueCountFrequency (%)
) 7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 14751
95.7%
Common 658
 
4.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 1846
12.5%
i 1649
11.2%
a 1507
10.2%
n 1258
8.5%
e 1205
8.2%
g 1093
7.4%
p 1030
 
7.0%
k 1007
 
6.8%
t 776
 
5.3%
o 724
 
4.9%
Other values (12) 2656
18.0%
Common
ValueCountFrequency (%)
464
70.5%
- 79
 
12.0%
/ 73
 
11.1%
, 14
 
2.1%
( 7
 
1.1%
) 7
 
1.1%
1 6
 
0.9%
2 6
 
0.9%
& 2
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15409
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 1846
12.0%
i 1649
10.7%
a 1507
9.8%
n 1258
 
8.2%
e 1205
 
7.8%
g 1093
 
7.1%
p 1030
 
6.7%
k 1007
 
6.5%
t 776
 
5.0%
o 724
 
4.7%
Other values (21) 3314
21.5%

SecondLargestPropertyUseTypeGFA
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct1352
Distinct (%)80.5%
Missing1697
Missing (%)50.3%
Infinite0
Infinite (%)0.0%
Mean28444.076
Minimum0
Maximum686750
Zeros126
Zeros (%)3.7%
Negative0
Negative (%)0.0%
Memory size26.5 KiB
2023-06-12T10:20:56.152003image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15000
median10664
Q326640
95-th percentile117338.6
Maximum686750
Range686750
Interquartile range (IQR)21640

Descriptive statistics

Standard deviation54392.918
Coefficient of variation (CV)1.9122758
Kurtosis36.302083
Mean28444.076
Median Absolute Deviation (MAD)7564
Skewness5.0334807
Sum47757603
Variance2.9585895 × 109
MonotonicityNot monotonic
2023-06-12T10:20:56.929729image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 126
 
3.7%
5000 14
 
0.4%
7200 12
 
0.4%
15000 12
 
0.4%
6000 12
 
0.4%
7000 9
 
0.3%
10000 8
 
0.2%
8000 7
 
0.2%
4000 7
 
0.2%
1500 6
 
0.2%
Other values (1342) 1466
43.4%
(Missing) 1697
50.3%
ValueCountFrequency (%)
0 126
3.7%
2 1
 
< 0.1%
40 1
 
< 0.1%
200 1
 
< 0.1%
220 1
 
< 0.1%
300 1
 
< 0.1%
320 1
 
< 0.1%
363 1
 
< 0.1%
400 2
 
0.1%
406 1
 
< 0.1%
ValueCountFrequency (%)
686750 1
< 0.1%
639931 1
< 0.1%
441551 1
< 0.1%
438756 1
< 0.1%
389860 1
< 0.1%
387651 1
< 0.1%
380639 1
< 0.1%
377046 1
< 0.1%
348788 1
< 0.1%
340236 1
< 0.1%

ThirdLargestPropertyUseType
Categorical

HIGH CORRELATION  MISSING 

Distinct44
Distinct (%)7.4%
Missing2780
Missing (%)82.3%
Memory size26.5 KiB
retail store
110 
office
105 
parking
71 
restaurant
56 
other
49 
Other values (39)
205 

Length

Max length52
Median length27
Mean length11.996644
Min length5

Characters and Unicode

Total characters7150
Distinct characters30
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)1.5%

Sample

1st rowrestaurant
2nd rowswimming pool
3rd rowdata center
4th rowswimming pool
5th rowoffice

Common Values

ValueCountFrequency (%)
retail store 110
 
3.3%
office 105
 
3.1%
parking 71
 
2.1%
restaurant 56
 
1.7%
other 49
 
1.5%
swimming pool 29
 
0.9%
non-refrigerated warehouse 18
 
0.5%
medical office 17
 
0.5%
data center 14
 
0.4%
multifamily housing 12
 
0.4%
Other values (34) 115
 
3.4%
(Missing) 2780
82.3%

Length

2023-06-12T10:20:57.676664image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
office 127
13.1%
store 115
 
11.9%
retail 110
 
11.3%
other 77
 
7.9%
parking 71
 
7.3%
restaurant 59
 
6.1%
swimming 29
 
3.0%
pool 29
 
3.0%
28
 
2.9%
warehouse 20
 
2.1%
Other values (59) 305
31.4%

Most occurring characters

ValueCountFrequency (%)
e 853
11.9%
r 722
 
10.1%
t 619
 
8.7%
i 570
 
8.0%
a 568
 
7.9%
o 538
 
7.5%
374
 
5.2%
s 361
 
5.0%
n 349
 
4.9%
f 323
 
4.5%
Other values (20) 1873
26.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 6623
92.6%
Space Separator 374
 
5.2%
Other Punctuation 79
 
1.1%
Dash Punctuation 58
 
0.8%
Open Punctuation 6
 
0.1%
Close Punctuation 6
 
0.1%
Decimal Number 4
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 853
12.9%
r 722
10.9%
t 619
9.3%
i 570
8.6%
a 568
8.6%
o 538
 
8.1%
s 361
 
5.5%
n 349
 
5.3%
f 323
 
4.9%
l 308
 
4.7%
Other values (12) 1412
21.3%
Other Punctuation
ValueCountFrequency (%)
/ 67
84.8%
, 12
 
15.2%
Decimal Number
ValueCountFrequency (%)
1 2
50.0%
2 2
50.0%
Space Separator
ValueCountFrequency (%)
374
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 58
100.0%
Open Punctuation
ValueCountFrequency (%)
( 6
100.0%
Close Punctuation
ValueCountFrequency (%)
) 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 6623
92.6%
Common 527
 
7.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 853
12.9%
r 722
10.9%
t 619
9.3%
i 570
8.6%
a 568
8.6%
o 538
 
8.1%
s 361
 
5.5%
n 349
 
5.3%
f 323
 
4.9%
l 308
 
4.7%
Other values (12) 1412
21.3%
Common
ValueCountFrequency (%)
374
71.0%
/ 67
 
12.7%
- 58
 
11.0%
, 12
 
2.3%
( 6
 
1.1%
) 6
 
1.1%
1 2
 
0.4%
2 2
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7150
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 853
11.9%
r 722
 
10.1%
t 619
 
8.7%
i 570
 
8.0%
a 568
 
7.9%
o 538
 
7.5%
374
 
5.2%
s 361
 
5.0%
n 349
 
4.9%
f 323
 
4.5%
Other values (20) 1873
26.2%

ThirdLargestPropertyUseTypeGFA
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct501
Distinct (%)84.1%
Missing2780
Missing (%)82.3%
Infinite0
Infinite (%)0.0%
Mean11738.675
Minimum0
Maximum459748
Zeros48
Zeros (%)1.4%
Negative0
Negative (%)0.0%
Memory size26.5 KiB
2023-06-12T10:20:58.338785image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12239
median5043
Q310138.75
95-th percentile41654.5
Maximum459748
Range459748
Interquartile range (IQR)7899.75

Descriptive statistics

Standard deviation29331.199
Coefficient of variation (CV)2.4986805
Kurtosis114.18711
Mean11738.675
Median Absolute Deviation (MAD)3590.5
Skewness9.1969358
Sum6996250.4
Variance8.6031925 × 108
MonotonicityNot monotonic
2023-06-12T10:20:59.036080image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 48
 
1.4%
6000 7
 
0.2%
5000 6
 
0.2%
2000 5
 
0.1%
3000 5
 
0.1%
1000 4
 
0.1%
9000 4
 
0.1%
6200 3
 
0.1%
1250 3
 
0.1%
1500 3
 
0.1%
Other values (491) 508
 
15.0%
(Missing) 2780
82.3%
ValueCountFrequency (%)
0 48
1.4%
182 1
 
< 0.1%
187 1
 
< 0.1%
240 1
 
< 0.1%
250 1
 
< 0.1%
285 1
 
< 0.1%
294 1
 
< 0.1%
400 1
 
< 0.1%
404 1
 
< 0.1%
436 1
 
< 0.1%
ValueCountFrequency (%)
459748 1
< 0.1%
303910 1
< 0.1%
220303 1
< 0.1%
177210 1
< 0.1%
141450 1
< 0.1%
133598 1
< 0.1%
103478 1
< 0.1%
103200 1
< 0.1%
88901 1
< 0.1%
84051.89844 1
< 0.1%

YearsENERGYSTARCertified
Real number (ℝ)

Distinct12
Distinct (%)10.1%
Missing3257
Missing (%)96.5%
Infinite0
Infinite (%)0.0%
Mean3.0588235
Minimum1
Maximum15
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.5 KiB
2023-06-12T10:20:59.608464image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q34
95-th percentile8
Maximum15
Range14
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.6112343
Coefficient of variation (CV)0.85367274
Kurtosis4.4140338
Mean3.0588235
Median Absolute Deviation (MAD)1
Skewness1.8865609
Sum364
Variance6.8185444
MonotonicityNot monotonic
2023-06-12T10:21:00.122085image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1 42
 
1.2%
2 27
 
0.8%
3 14
 
0.4%
4 10
 
0.3%
5 7
 
0.2%
7 7
 
0.2%
6 5
 
0.1%
9 2
 
0.1%
8 2
 
0.1%
10 1
 
< 0.1%
Other values (2) 2
 
0.1%
(Missing) 3257
96.5%
ValueCountFrequency (%)
1 42
1.2%
2 27
0.8%
3 14
 
0.4%
4 10
 
0.3%
5 7
 
0.2%
6 5
 
0.1%
7 7
 
0.2%
8 2
 
0.1%
9 2
 
0.1%
10 1
 
< 0.1%
ValueCountFrequency (%)
15 1
 
< 0.1%
13 1
 
< 0.1%
10 1
 
< 0.1%
9 2
 
0.1%
8 2
 
0.1%
7 7
0.2%
6 5
 
0.1%
5 7
0.2%
4 10
0.3%
3 14
0.4%

ENERGYSTARScore
Real number (ℝ)

Distinct100
Distinct (%)3.9%
Missing843
Missing (%)25.0%
Infinite0
Infinite (%)0.0%
Mean67.918674
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.5 KiB
2023-06-12T10:21:00.813075image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile12
Q153
median75
Q390
95-th percentile99
Maximum100
Range99
Interquartile range (IQR)37

Descriptive statistics

Standard deviation26.873271
Coefficient of variation (CV)0.39566837
Kurtosis-0.21956688
Mean67.918674
Median Absolute Deviation (MAD)17
Skewness-0.85946132
Sum172038
Variance722.17269
MonotonicityNot monotonic
2023-06-12T10:21:01.542936image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 109
 
3.2%
98 72
 
2.1%
96 64
 
1.9%
89 58
 
1.7%
93 57
 
1.7%
92 53
 
1.6%
95 51
 
1.5%
94 49
 
1.5%
91 49
 
1.5%
99 49
 
1.5%
Other values (90) 1922
56.9%
(Missing) 843
25.0%
ValueCountFrequency (%)
1 36
1.1%
2 10
 
0.3%
3 13
 
0.4%
4 5
 
0.1%
5 10
 
0.3%
6 8
 
0.2%
7 10
 
0.3%
8 10
 
0.3%
9 5
 
0.1%
10 10
 
0.3%
ValueCountFrequency (%)
100 109
3.2%
99 49
1.5%
98 72
2.1%
97 48
1.4%
96 64
1.9%
95 51
1.5%
94 49
1.5%
93 57
1.7%
92 53
1.6%
91 49
1.5%

ComplianceStatus
Categorical

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size26.5 KiB
compliant
3211 
error - correct default data
 
113
non-compliant
 
37
missing data
 
15

Length

Max length28
Median length9
Mean length9.693128
Min length9

Characters and Unicode

Total characters32724
Distinct characters18
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowcompliant
2nd rowcompliant
3rd rowcompliant
4th rowcompliant
5th rowcompliant

Common Values

ValueCountFrequency (%)
compliant 3211
95.1%
error - correct default data 113
 
3.3%
non-compliant 37
 
1.1%
missing data 15
 
0.4%

Length

2023-06-12T10:21:02.245615image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-12T10:21:02.918877image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
compliant 3211
83.6%
data 128
 
3.3%
error 113
 
2.9%
113
 
2.9%
correct 113
 
2.9%
default 113
 
2.9%
non-compliant 37
 
1.0%
missing 15
 
0.4%

Most occurring characters

ValueCountFrequency (%)
a 3617
11.1%
t 3602
11.0%
o 3511
10.7%
c 3474
10.6%
l 3361
10.3%
n 3337
10.2%
i 3278
10.0%
m 3263
10.0%
p 3248
9.9%
r 565
 
1.7%
Other values (8) 1468
4.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 32107
98.1%
Space Separator 467
 
1.4%
Dash Punctuation 150
 
0.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 3617
11.3%
t 3602
11.2%
o 3511
10.9%
c 3474
10.8%
l 3361
10.5%
n 3337
10.4%
i 3278
10.2%
m 3263
10.2%
p 3248
10.1%
r 565
 
1.8%
Other values (6) 851
 
2.7%
Space Separator
ValueCountFrequency (%)
467
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 150
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 32107
98.1%
Common 617
 
1.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 3617
11.3%
t 3602
11.2%
o 3511
10.9%
c 3474
10.8%
l 3361
10.5%
n 3337
10.4%
i 3278
10.2%
m 3263
10.2%
p 3248
10.1%
r 565
 
1.8%
Other values (6) 851
 
2.7%
Common
ValueCountFrequency (%)
467
75.7%
- 150
 
24.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 32724
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 3617
11.1%
t 3602
11.0%
o 3511
10.7%
c 3474
10.6%
l 3361
10.3%
n 3337
10.2%
i 3278
10.0%
m 3263
10.0%
p 3248
9.9%
r 565
 
1.7%
Other values (8) 1468
4.5%

TotalGHGEmissions
Real number (ℝ)

Distinct2818
Distinct (%)83.7%
Missing9
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean119.72397
Minimum-0.8
Maximum16870.98
Zeros9
Zeros (%)0.3%
Negative1
Negative (%)< 0.1%
Memory size26.5 KiB
2023-06-12T10:21:03.609643image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-0.8
5-th percentile3.78
Q19.495
median33.92
Q393.94
95-th percentile392.797
Maximum16870.98
Range16871.78
Interquartile range (IQR)84.445

Descriptive statistics

Standard deviation538.83223
Coefficient of variation (CV)4.5006211
Kurtosis474.89222
Mean119.72397
Median Absolute Deviation (MAD)27.94
Skewness19.481875
Sum403110.61
Variance290340.17
MonotonicityNot monotonic
2023-06-12T10:21:04.409759image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 9
 
0.3%
3.95 7
 
0.2%
4.2 6
 
0.2%
5.46 6
 
0.2%
4.74 5
 
0.1%
5.07 5
 
0.1%
6.18 5
 
0.1%
3.63 5
 
0.1%
4.8 5
 
0.1%
4.02 5
 
0.1%
Other values (2808) 3309
98.0%
(Missing) 9
 
0.3%
ValueCountFrequency (%)
-0.8 1
 
< 0.1%
0 9
0.3%
0.09 1
 
< 0.1%
0.12 1
 
< 0.1%
0.17 1
 
< 0.1%
0.31 1
 
< 0.1%
0.4 1
 
< 0.1%
0.5 1
 
< 0.1%
0.63 1
 
< 0.1%
0.68 1
 
< 0.1%
ValueCountFrequency (%)
16870.98 1
< 0.1%
12307.16 1
< 0.1%
11140.56 1
< 0.1%
10734.57 1
< 0.1%
8145.52 1
< 0.1%
6330.91 1
< 0.1%
4906.33 1
< 0.1%
3995.45 1
< 0.1%
3768.66 1
< 0.1%
3278.11 1
< 0.1%

GHGEmissionsIntensity
Real number (ℝ)

Distinct511
Distinct (%)15.2%
Missing9
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean1.1759162
Minimum-0.02
Maximum34.09
Zeros12
Zeros (%)0.4%
Negative1
Negative (%)< 0.1%
Memory size26.5 KiB
2023-06-12T10:21:05.209098image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-0.02
5-th percentile0.13
Q10.21
median0.61
Q31.37
95-th percentile3.961
Maximum34.09
Range34.11
Interquartile range (IQR)1.16

Descriptive statistics

Standard deviation1.8214518
Coefficient of variation (CV)1.5489639
Kurtosis57.372156
Mean1.1759162
Median Absolute Deviation (MAD)0.44
Skewness5.5931448
Sum3959.31
Variance3.3176866
MonotonicityNot monotonic
2023-06-12T10:21:06.014726image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.17 99
 
2.9%
0.15 99
 
2.9%
0.16 96
 
2.8%
0.18 86
 
2.5%
0.19 78
 
2.3%
0.2 70
 
2.1%
0.13 66
 
2.0%
0.14 62
 
1.8%
0.21 60
 
1.8%
0.22 54
 
1.6%
Other values (501) 2597
76.9%
ValueCountFrequency (%)
-0.02 1
 
< 0.1%
0 12
0.4%
0.01 4
 
0.1%
0.02 4
 
0.1%
0.03 7
0.2%
0.04 9
0.3%
0.05 9
0.3%
0.06 16
0.5%
0.07 8
0.2%
0.08 8
0.2%
ValueCountFrequency (%)
34.09 1
< 0.1%
25.71 1
< 0.1%
16.99 1
< 0.1%
16.93 1
< 0.1%
16.91 1
< 0.1%
16.38 1
< 0.1%
15.42 1
< 0.1%
14.94 1
< 0.1%
14.89 1
< 0.1%
14.32 1
< 0.1%

Interactions

2023-06-12T10:20:22.294955image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T10:18:50.390078image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T10:18:57.972211image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T10:19:05.085585image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T10:19:12.822951image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T10:19:20.150526image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T10:19:27.394682image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T10:19:35.223801image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T10:19:42.610815image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T10:19:51.115413image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T10:19:58.929367image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T10:20:06.188576image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T10:20:14.311017image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T10:20:22.889309image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T10:18:50.971740image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T10:18:58.511474image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T10:19:05.690386image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T10:19:13.427513image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T10:19:20.696330image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T10:19:27.962894image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T10:19:35.773931image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T10:19:43.223685image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T10:19:51.696753image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T10:19:59.446156image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T10:20:06.866989image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T10:20:14.904101image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T10:20:23.523973image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T10:18:51.609338image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T10:18:59.028247image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T10:19:06.254480image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T10:19:13.996408image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T10:19:21.240298image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T10:19:28.518678image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T10:19:36.404411image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T10:19:43.927233image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T10:19:52.291414image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T10:19:59.922946image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T10:20:07.609910image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T10:20:15.551831image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T10:20:24.130535image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T10:18:52.209543image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T10:18:59.549916image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T10:19:06.776819image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T10:19:14.549052image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T10:19:21.835758image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T10:19:29.179709image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T10:19:36.943347image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T10:19:44.535913image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T10:19:52.889409image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T10:20:00.414633image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T10:20:08.225214image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T10:20:16.168007image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T10:20:24.714316image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T10:18:52.752559image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T10:19:00.067700image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T10:19:07.320136image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T10:19:15.110597image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T10:19:22.425727image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T10:19:29.715233image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T10:19:37.471926image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T10:19:45.129856image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T10:19:53.627272image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T10:20:00.940619image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T10:20:08.884585image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T10:20:16.804773image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T10:20:25.295019image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T10:18:53.302769image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T10:19:00.618008image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T10:19:08.007301image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T10:19:15.629939image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T10:19:22.937859image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T10:19:30.320750image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T10:19:38.024033image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T10:19:45.762752image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T10:19:54.260087image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T10:20:01.492140image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T10:20:09.497966image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T10:20:17.442576image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T10:20:25.846937image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T10:18:53.909473image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T10:19:01.173490image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T10:19:08.654924image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T10:19:16.155932image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T10:19:23.460828image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T10:19:30.855045image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T10:19:38.532053image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T10:19:46.395208image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T10:19:55.019522image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T10:20:02.094542image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T10:20:10.159209image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T10:20:18.021085image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T10:20:26.521376image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T10:18:54.501092image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T10:19:01.699713image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T10:19:09.195207image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T10:19:16.696611image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T10:19:23.965313image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T10:19:31.497552image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T10:19:39.068367image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T10:19:47.092350image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T10:19:55.601374image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T10:20:02.745853image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T10:20:10.802852image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T10:20:18.598257image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T10:20:27.130049image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T10:18:55.140639image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T10:19:02.314414image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T10:19:09.812681image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T10:19:17.278552image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T10:19:24.536349image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T10:19:32.154404image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T10:19:39.695598image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T10:19:47.752869image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T10:19:56.155889image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T10:20:03.333415image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T10:20:11.453372image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T10:20:19.289271image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T10:20:27.659301image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T10:18:55.658250image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T10:19:02.838870image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T10:19:10.357592image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T10:19:17.817676image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T10:19:25.043811image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T10:19:32.753457image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T10:19:40.254431image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T10:19:48.362407image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T10:19:56.738979image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T10:20:03.926976image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T10:20:11.959564image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T10:20:19.827566image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T10:20:28.209113image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T10:18:56.247206image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T10:19:03.349734image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T10:19:11.064258image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T10:19:18.361372image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T10:19:25.545270image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T10:19:33.343873image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T10:19:40.795679image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T10:19:49.177156image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T10:19:57.304859image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T10:20:04.468995image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T10:20:12.495509image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T10:20:20.362141image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T10:20:28.830327image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T10:18:56.783001image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T10:19:03.924964image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T10:19:11.685621image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T10:19:18.922824image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T10:19:26.124526image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T10:19:34.015075image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T10:19:41.362300image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T10:19:49.788480image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T10:19:57.849074image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T10:20:05.011481image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T10:20:13.041980image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T10:20:21.011939image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T10:20:29.441077image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T10:18:57.354028image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T10:19:04.491008image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T10:19:12.255992image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T10:19:19.574773image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T10:19:26.768477image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T10:19:34.606307image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T10:19:41.932867image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T10:19:50.487141image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T10:19:58.384318image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T10:20:05.557750image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T10:20:13.692458image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T10:20:21.678460image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-06-12T10:21:06.853437image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
YearBuiltNumberofBuildingsNumberofFloorsPropertyGFATotalPropertyGFAParkingPropertyGFABuilding(s)LargestPropertyUseTypeGFASecondLargestPropertyUseTypeGFAThirdLargestPropertyUseTypeGFAYearsENERGYSTARCertifiedENERGYSTARScoreTotalGHGEmissionsGHGEmissionsIntensityBuildingTypePrimaryPropertyTypeNeighborhoodSecondLargestPropertyUseTypeThirdLargestPropertyUseTypeComplianceStatus
YearBuilt1.0000.0350.2920.3120.2420.2830.2930.3270.098-0.0690.0750.027-0.1960.1580.1860.1760.1910.1260.059
NumberofBuildings0.0351.000-0.0230.0690.0060.0680.0830.0250.004-0.0830.0370.0490.0160.2400.1360.0140.1810.9640.000
NumberofFloors0.292-0.0231.0000.4400.2620.4310.4140.4350.2120.4940.1180.175-0.1020.2620.2760.1400.0000.0270.000
PropertyGFATotal0.3120.0690.4401.0000.3450.9830.9290.6820.4800.4850.0740.5790.0680.1440.1730.0570.0000.1050.000
PropertyGFAParking0.2420.0060.2620.3451.0000.2210.2730.3720.2660.4680.0100.208-0.0090.0520.1550.0580.0000.0000.000
PropertyGFABuilding(s)0.2830.0680.4310.9830.2211.0000.9270.6440.4520.4030.0740.5740.0740.1660.1900.0470.0000.0000.000
LargestPropertyUseTypeGFA0.2930.0830.4140.9290.2730.9271.0000.5650.3460.4350.0850.5650.0880.1480.2260.0470.0000.0000.000
SecondLargestPropertyUseTypeGFA0.3270.0250.4350.6820.3720.6440.5651.0000.6090.4350.1790.415-0.0040.1240.2350.0570.0340.1770.079
ThirdLargestPropertyUseTypeGFA0.0980.0040.2120.4800.2660.4520.3460.6091.0000.3380.1190.3530.0150.1780.0000.0000.1770.1220.000
YearsENERGYSTARCertified-0.069-0.0830.4940.4850.4680.4030.4350.4350.3381.0000.1890.274-0.2950.0000.0000.0000.0000.0000.000
ENERGYSTARScore0.0750.0370.1180.0740.0100.0740.0850.1790.1190.1891.000-0.118-0.2370.1150.1180.0540.0630.1390.120
TotalGHGEmissions0.0270.0490.1750.5790.2080.5740.5650.4150.3530.274-0.1181.0000.8240.1260.2590.0000.0000.3580.000
GHGEmissionsIntensity-0.1960.016-0.1020.068-0.0090.0740.088-0.0040.015-0.295-0.2370.8241.0000.1260.2680.0000.1610.1180.008
BuildingType0.1580.2400.2620.1440.0520.1660.1480.1240.1780.0000.1150.1260.1261.0000.7320.2030.3150.2220.451
PrimaryPropertyType0.1860.1360.2760.1730.1550.1900.2260.2350.0000.0000.1180.2590.2680.7321.0000.2340.2110.2390.381
Neighborhood0.1760.0140.1400.0570.0580.0470.0470.0570.0000.0000.0540.0000.0000.2030.2341.0000.1520.0000.098
SecondLargestPropertyUseType0.1910.1810.0000.0000.0000.0000.0000.0340.1770.0000.0630.0000.1610.3150.2110.1521.0000.1750.156
ThirdLargestPropertyUseType0.1260.9640.0270.1050.0000.0000.0000.1770.1220.0000.1390.3580.1180.2220.2390.0000.1751.0000.000
ComplianceStatus0.0590.0000.0000.0000.0000.0000.0000.0790.0000.0000.1200.0000.0080.4510.3810.0980.1560.0001.000

Missing values

2023-06-12T10:20:30.422285image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-06-12T10:20:32.533608image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-06-12T10:20:34.023245image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

BuildingTypePrimaryPropertyTypeNeighborhoodYearBuiltNumberofBuildingsNumberofFloorsPropertyGFATotalPropertyGFAParkingPropertyGFABuilding(s)ListOfAllPropertyUseTypesLargestPropertyUseTypeLargestPropertyUseTypeGFASecondLargestPropertyUseTypeSecondLargestPropertyUseTypeGFAThirdLargestPropertyUseTypeThirdLargestPropertyUseTypeGFAYearsENERGYSTARCertifiedENERGYSTARScoreComplianceStatusTotalGHGEmissionsGHGEmissionsIntensity
0nonresidentialhoteldowntown19271.01288434088434hotelhotel88434.0NaNNaNNaNNaNNaN60.0compliant249.982.83
1nonresidentialhoteldowntown19961.0111035661506488502hotel, parking, restauranthotel83880.0parking15064.0restaurant4622.0NaN61.0compliant295.862.86
2nonresidentialhoteldowntown19691.041956110196718759392hotelhotel756493.0NaNNaNNaNNaNNaN43.0compliant2089.282.19
3nonresidentialhoteldowntown19261.01061320061320hotelhotel61320.0NaNNaNNaNNaNNaN56.0compliant286.434.67
4nonresidentialhoteldowntown19801.01817558062000113580hotel, parking, swimming poolhotel123445.0parking68009.0swimming pool0.0NaN75.0compliant505.012.88
5nonresidential cosotherdowntown19991.02972883719860090police stationpolice station88830.0NaNNaNNaNNaNNaNNaNcompliant301.813.10
6nonresidentialhoteldowntown19261.01183008083008hotelhotel81352.0NaNNaNNaNNaNNaN27.0compliant176.142.12
7nonresidentialotherdowntown19261.081027610102761other - entertainment/public assemblyother - entertainment/public assembly102761.0NaNNaNNaNNaNNaNNaNcompliant221.512.16
8nonresidentialhoteldowntown19041.0151639840163984hotelhotel163984.0NaNNaNNaNNaNNaN43.0compliant392.162.39
9multifamily mr (5-9)mid-rise multifamilydowntown19101.0663712149662216multifamily housingmultifamily housing56132.0NaNNaNNaNNaNNaN1.0compliant151.122.37
BuildingTypePrimaryPropertyTypeNeighborhoodYearBuiltNumberofBuildingsNumberofFloorsPropertyGFATotalPropertyGFAParkingPropertyGFABuilding(s)ListOfAllPropertyUseTypesLargestPropertyUseTypeLargestPropertyUseTypeGFASecondLargestPropertyUseTypeSecondLargestPropertyUseTypeGFAThirdLargestPropertyUseTypeThirdLargestPropertyUseTypeGFAYearsENERGYSTARCertifiedENERGYSTARScoreComplianceStatusTotalGHGEmissionsGHGEmissionsIntensity
3366nonresidential cosofficemagnolia / queen anne19521.0113661013661officeoffice13661.0NaNNaNNaNNaNNaN75.0error - correct default data3.500.26
3367nonresidential cosothereast19121.0123445023445other - recreationother - recreation23445.0NaNNaNNaNNaNNaNNaNcompliant259.2211.06
3368nonresidential cosmixed use propertycentral19941.0120050020050fitness center/health club/gym, office, other - recreation, other - technology/scienceother - recreation8108.0fitness center/health club/gym7726.0office3779.0NaNNaNcompliant60.813.03
3369nonresidential cosofficesoutheast19601.0115398015398officeoffice15398.0NaNNaNNaNNaNNaN93.0error - correct default data7.790.51
3370nonresidential cosotherdelridge neighborhoods19821.0118261018261other - recreationother - recreation18261.0NaNNaNNaNNaNNaNNaNcompliant20.331.11
3371nonresidential cosofficegreater duwamish19901.0112294012294officeoffice12294.0NaNNaNNaNNaNNaN46.0error - correct default data20.941.70
3372nonresidential cosotherdowntown20041.0116000016000other - recreationother - recreation16000.0NaNNaNNaNNaNNaNNaNcompliant32.172.01
3373nonresidential cosothermagnolia / queen anne19741.0113157013157fitness center/health club/gym, other - recreation, swimming poolother - recreation7583.0fitness center/health club/gym5574.0swimming pool0.0NaNNaNcompliant223.5416.99
3374nonresidential cosmixed use propertygreater duwamish19891.0114101014101fitness center/health club/gym, food service, office, other - recreation, pre-school/daycareother - recreation6601.0fitness center/health club/gym6501.0pre-school/daycare484.0NaNNaNcompliant22.111.57
3375nonresidential cosmixed use propertygreater duwamish19381.0118258018258fitness center/health club/gym, food service, office, other - recreation, pre-school/daycareother - recreation8271.0fitness center/health club/gym8000.0pre-school/daycare1108.0NaNNaNcompliant41.272.26